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Sunny Arokia Swamy Bellary has completed

Introduction to Data Visualization with Matplotlib

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4 hours

3,600 XP

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Course Description

Visualizing data in plots and figures exposes the underlying patterns in the data and provides insights. Good visualizations also help you communicate your data to others, and are useful to data analysts and other consumers of the data. In this course, you will learn how to use Matplotlib, a powerful Python data visualization library. Matplotlib provides the building blocks to create rich visualizations of many different kinds of datasets. You will learn how to create visualizations for different kinds of data and how to customize, automate, and share these visualizations.

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1
Introduction to Matplotlib
Free
This chapter introduces the Matplotlib visualization library and demonstrates how to use it with data.
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Introduction to data visualization with Matplotlib
50 xp
Using the matplotlib.pyplot interface
100 xp
Adding data to an Axes object
100 xp
Customizing your plots
50 xp
Customizing data appearance
100 xp
Customizing axis labels and adding titles
100 xp
Small multiples
50 xp
Creating a grid of subplots
50 xp
Creating small multiples with plt.subplots
100 xp
Small multiples with shared y axis
100 xp
2
Plotting time-series
Time series data is data that is recorded. Visualizing this type of data helps clarify trends and illuminates relationships between data.
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Plotting time-series data
50 xp
Read data with a time index
100 xp
Plot time-series data
100 xp
Using a time index to zoom in
100 xp
Plotting time-series with different variables
50 xp
Plotting two variables
100 xp
Defining a function that plots time-series data
100 xp
Using a plotting function
100 xp
Annotating time-series data
50 xp
Annotating a plot of time-series data
100 xp
Plotting time-series: putting it all together
100 xp
3
Quantitative comparisons and statistical visualizations
Visualizations can be used to compare data in a quantitative manner. This chapter explains several methods for quantitative visualizations.
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Quantitative comparisons: bar-charts
50 xp
Bar chart
100 xp
Stacked bar chart
100 xp
Quantitative comparisons: histograms
50 xp
Creating histograms
100 xp
"Step" histogram
100 xp
Statistical plotting
50 xp
Adding error-bars to a bar chart
100 xp
Adding error-bars to a plot
100 xp
Creating boxplots
100 xp
Quantitative comparisons: scatter plots
50 xp
Simple scatter plot
100 xp
Encoding time by color
100 xp
4
Sharing visualizations with others
This chapter shows you how to share your visualizations with others: how to save your figures as files, how to adjust their look and feel, and how to automate their creation based on input data.
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Preparing your figures to share with others
50 xp
Selecting a style for printing
50 xp
Switching between styles
100 xp
Saving your visualizations
50 xp
Saving a file several times
100 xp
Save a figure with different sizes
100 xp
Automating figures from data
50 xp
Unique values of a column
100 xp
Automate your visualization
100 xp
Where to go next
50 xp

In the following tracks

Associate Data Scientist Data Visualization

Datasets

Seattle weather Austin weather Climate data Medals by country Medalist weights

Collaborators

Chester Ismay

Amy Peterson

Prerequisites

Introduction to Python

Senior Data Scientist, University of Washington

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